Solid-state image sensors have found widespread use in cameras and imaging systems. Some solid-state image sensors are composed of a matrix of photosensitive elements in series with amplifying and switching components. The photosensitive elements may be, for example, photo-diodes, phototransistors, photo-gates or the like. Typically, an image is focused on an array of picture elements (pixels) containing photosensitive elements, such that each photosensitive element in the array of pixels receives electromagnetic energy from a portion of the focused image. Each photosensitive element converts a portion of the electromagnetic energy it receives into electron-hole pairs and produces a charge or current that is proportional to the intensity of the electromagnetic energy it receives.
Solid-state image sensors may be fabricated using several different device technologies, including CCD (charge-coupled device) technology and MOS (metal-oxide semiconductor) and/or CMOS (complementary metal-oxide semiconductor) technologies. Typically, these technologies are silicon based. However, other material systems, such as III-V or II-VI compound semiconductor systems may be used, depending on the specific imaging application and/or portion of the electromagnetic spectrum to be imaged (e.g., visible light photography, ultraviolet imaging, infrared imaging, etc.).
The pixels in silicon-based image sensors that are used for light photography are inherently panchromatic. They respond to a range of electromagnetic wavelengths that include the entire visible spectrum as well as portions of the infrared and ultraviolet bands. To produce a color image, a color filter array (CFA) can be located in front of the pixel array. Typically, the CFA consists of an array of red (R), green (G) and blue (B) polymer filters deposited on top of the pixel array, with one filter covering each pixel in the pixel array in a prescribed pattern. The polymer color filter over each pixel passes a limited range of electromagnetic wavelengths (e.g., red or green or blue) and reflects and/or absorbs other wavelengths. CFA's can also be made with complementary color filters such as cyan, magenta and yellow, or any other color system. However, the RGB system is used in the majority of color imaging systems.
The apparent color of an object depends on the color of the light that illuminates the object. If the source of the illumination (the illuminant) is not white, then the object will have a color cast that reflects the color of the illuminant. In photography, white balance is the process of removing unrealistic color casts arising from different lighting conditions so that a white object appears white in photographs of the object. If the white balance is correct, then all of the other colors in the photograph are rendered more accurately.
Illuminants are characterized by their “correlated color temperature.” Color temperature describes the spectrum of light that a black body emits at that surface temperature (a black body is an idealized absorber/radiator of electromagnetic energy). The light emitted from a black body appears white to the human eye at approximately 6500 degrees Kelvin. At lower temperatures, the black body spectrum shifts toward the red end of the visible spectrum. At higher temperatures, the black body spectrum shifts toward the blue end of the visible spectrum. The correlated color temperature of an illuminant is the color temperature of a black body with the same (or approximately the same) apparent color as the illuminant. For example, daylight (clear day with the sun overhead) has a correlated color temperature in the range of 5000-6500 Kelvin, candlelight has a correlated color temperature in the range of 1000-2000 Kelvin and fluorescent light has a correlated color temperature in the range of 4000-5000 Kelvin.
As noted above, digital cameras typically utilize arrays of red, green and blue pixels to capture the colors of an image (where the red/green/blue ratio determines the apparent color). In digital photography, the current industry standard for white balance in low-end cameras is the well known “grey world” algorithm. This algorithm assumes that, on average, a scene will contain equal amounts of red, green and blue, so that that the average color of the scene is grey. Correction factors for red, green and blue are calculated such that this assumption is imposed on the image. Although this is a very simple algorithm, and leads to reasonably good results on most scenes, the algorithm fails completely on scenes with a dominant color like a blue sky, green grass or skin in a close up portrait because the average color in these cases is far from grey.